2022 - T3 - WS1 - Non-Linear and High Dimensional Inference

Collection 2022 - T3 - WS1 - Non-Linear and High Dimensional Inference

Organisateur(s) Aamari, Eddie ; Aaron, Catherine ; Chazal, Frédéric ; Fischer, Aurélie ; Hoffmann, Marc ; Le Brigant, Alice ; Levrard, Clément ; Michel, Bertrand

Date(s) 03/10/2022 - 07/10/2022

URL associée https://indico.math.cnrs.fr/event/7545/

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Topologically penalized regression on manifolds

De Wolfgang Polonik

We study a regression problem on a compact manifold. In order to take advantage of the underlying geometry and topology of the data, we propose to perform the regression task on the basis of eigenfunctions of the Laplace-Beltrami operator of the manifold that are regularized with topological penalties. We will discuss the approach and the penalties, provide some supporting theory and illustrate the performance of the methodology on some data sets, illustrating the relevance of our approach in the case where the target function is "topologically smooth”. This is joint work with O. Hacquard, K. Balasubramanian, G. Blanchard and C. Levrard

Informations sur la vidéo

Date de captation 04/10/2022
Date de publication 03/05/2024
Institut IHP
Langue Anglais
Audience Chercheurs, Doctorants
Réalisateur(s) Alexandre Duplessis, Marco Perez
Format MP4
Lieu Amphitheater Hermite, IHP

Données de citation

DOI 10.57987/IHP.2022.T3.WS1.006
Citer cette vidéo Polonik, Wolfgang (04/10/2022). Topologically penalized regression on manifolds. IHP. Audiovisual resource. DOI: 10.57987/IHP.2022.T3.WS1.006
URL https://dx.doi.org/10.57987/IHP.2022.T3.WS1.006

Domaine(s)

Apprentissage

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